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Model Serving Runtimes

KServe provides a simple Kubernetes CRD to enable deploying single or multiple trained models onto model serving runtimes such as TFServing, TorchServe, Triton Inference Server. In addition KFServer is the Python model serving runtime implemented in KServe itself with prediction v1 protocol, MLServer implements the prediction v2 protocol with both REST and gRPC. These model serving runtimes are able to provide out-of-the-box model serving, but you could also choose to build your own model server for more complex use case. KServe provides basic API primitives to allow you easily build custom model serving runtime, you can use other tools like BentoML to build your custom model serving image.

After models are deployed with InferenceService, you get all the following serverless features provided by KServe.

  • Scale to and from Zero
  • Request based Autoscaling on CPU/GPU
  • Revision Management
  • Optimized Container
  • Batching
  • Request/Response logging
  • Traffic management
  • Security with AuthN/AuthZ
  • Distributed Tracing
  • Out-of-the-box metrics
  • Ingress/Egress control
Model Serving Runtime Exported model Prediction Protocol HTTP gRPC Versions
Triton Inference Server TensorFlow,TorchScript,ONNX v2 ✔ ✔ Compatibility Matrix
TFServing TensorFlow SavedModel v1 ✔ ✔ TFServing Versions
TorchServe Eager Model/TorchScript v1 ✔ ✔ 0.4.1
SKLearn MLServer Pickled Model v2 ✔ ✔ 0.23.1
XGBoost MLServer Saved Model v2 ✔ ✔ 1.1.1
SKLearn KFServer Pickled Model v1 ✔ -- 0.20.3
XGBoost KFServer Saved Model v1 ✔ -- 0.82
PMML KFServer PMML v1 ✔ -- PMML4.4.1
LightGBM KFServer Saved LightGBM Model v1 ✔ -- 2.3.1
Custom KFServer -- v1 ✔ -- --


The model serving runtime version can be overwritten with the runtimeVersion field on InferenceService yaml and we highly recommend setting this field for production services.

apiVersion: ""
kind: "InferenceService"
  name: "torchscript-cifar"
      storageUri: "gs://kfserving-examples/models/torchscript"
      runtimeVersion: 21.08-py3 
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